
"""
Created on 2018/1/27 22:33 星期六
@author: Matt  zhuhan1401@126.com
Description: 4.7  使用朴素贝叶斯分类器从个人广告中获取区域倾向
"""

from numpy import *
import random
import feedparser
import operator
import re

def textParse(bigString):
    listOfTokens=re.split(r'\W*',bigString)
    return [tok.lower() for tok in listOfTokens if len(tok)>2]  # 666

# 统计单词在文本中出现次数，取前30个
def calcMostFreq(vocabList,fullText):
    freqDict={}
    for token in vocabList:
        freqDict[token]=fullText.count(token)
    sortedFreq=sorted(freqDict.items(),key=operator.itemgetter(1),reverse=True)
    return sortedFreq[:30]

def createVocabList(dataSet):
    vocabSet=set([])
    for document in dataSet:
        vocabSet=vocabSet | set(document) # 并集
    return list(vocabSet) # 创建一个包含所有文档中出现的不重复词的列表

# 同一个词可能出现多次，由词集模型改为词袋模型
def bagOfWords2VecMN(vocabList,inputSet):
    returnVec=[0]*len(vocabList)
    for word in inputSet:
        if word in vocabList:
            returnVec[vocabList.index(word)]+=1
    return returnVec

# 朴素贝叶斯分类器训练函数  (文档矩阵，文档标签)
def trainNB0(trainMatrix,trainCategory):
    numTrainDocs=len(trainMatrix)
    numWords=len(trainMatrix[0]) # 长度相同，求[0]的即可
    pAbusive=sum(trainCategory)/float(numTrainDocs) # 侮辱性的为1，求sum得侮辱性句子出现的次数，pAbusive表示侮辱性句子出现的频率
    # 防止因为第一个值是0 导致最后乘积也都为0
    p0Num=ones(numWords)
    p1Num=ones(numWords)
    p0Denom=2.0
    p1Denom=2.0
    for i in range(numTrainDocs):
        if trainCategory[i]==1:
            p1Num+=trainMatrix[i] # 列向量相加
            p1Denom+=sum(trainMatrix[i])# todo
        else:
            p0Num+=trainMatrix[i]
            p0Denom+=sum(trainMatrix[i])
    # use log to avoid underflow
    p1Vect=log(p1Num/p1Denom)
    p0Vect=log(p0Num/p0Denom)
    return p0Vect,p1Vect,pAbusive

# 朴素贝叶斯分类函数
# 要分类的相邻V2C，计算的三个概率
def classifyNB(vec2Classify,p0Vec,p1Vec,pClass1):
    p1=sum(vec2Classify*p1Vec)+log(pClass1)
    p0=sum(vec2Classify*p0Vec)+log(1.0-pClass1)
    if p1>p0:
        return 1
    else:
        return 0

def localWords(feed1,feed0):
    docList=[];classList=[];fullText=[]
    minLen=min(len(feed1['entries']),len(feed0['entries']))
    for i in range(minLen):
        wordList=textParse(feed1['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(1)
        wordList = textParse(feed0['entries'][i]['summary'])
        docList.append(wordList)
        fullText.extend(wordList)
        classList.append(0)
    vocabList=createVocabList(docList)
    top30Words=calcMostFreq(vocabList,fullText)
    for pairW in top30Words:
        if pairW[0] in vocabList:
            vocabList.remove(pairW[0])
    trainingSet=list(range(2*minLen))
    testSet=[]
    for i in range(20):
        randIndex=int(random.uniform(0,len(trainingSet)))
        testSet.append(trainingSet[randIndex])
        del(trainingSet[randIndex])
    trainMat=[];trainClasses=[]
    for docIndex in trainingSet:
        trainMat.append(bagOfWords2VecMN(vocabList,docList[docIndex]))
        trainClasses.append(classList[docIndex])
    p0V, p1V, pSpam = trainNB0(array(trainMat), array(trainClasses))
    errorCount = 0
    # 对测试集分类
    for docIndex in testSet:
        wordVector = bagOfWords2VecMN(vocabList, docList[docIndex])
        if classifyNB(array(wordVector), p0V, p1V, pSpam) != classList[docIndex]:
            errorCount += 1
    print('the error count is %d, the error rate is : %f %% ' % (errorCount, (float(errorCount) / 100 * len(testSet))))
    return vocabList,p0V,p1V

# ...获取不到数据源 测试时entries长度为0 待吉时再测
ny=feedparser.parse('http://newyork.craigslist.org/stp/index.rss')
sf=feedparser.parse('http://sfbay.craigslist.org/stp/index.rss')
vocabList,pSF,pNY=localWords(ny,sf)

def getTopWords(ny,sf):
    vocabList,p0V,p1V=localWords(ny,sf)
    topNY=[];topSF=[]
    for i in range(len(p0V)):
        if (p0V[i]>-6.0):
            topSF.append((vocabList[i],p0V[i]))
        if (p1V[i]>-6.0):
            topSF.append((vocabList[i],p1V[i]))
    sortedSF=sorted(topSF,key=lambda pair:pair[1],reverse=True)
    print("--------SF---------")
    for item in sortedSF:
        print(item[0])
    sortedNY=sorted(topNY,key=lambda pair:pair[1],reverse=True)
    print("--------NY---------")
    for item in sortedNY:
        print(item[0])





















